With digital cameras, the problem of hand-shake blur has been addressed in several different ways. In the case of high-end and middle-end cameras, a built-in accelerometer or gyro type circuitry, together with an optical stabilizer, has been used. With this approach, the gyro would measure the camera motion and, in accordance with the gyro signal, a controller would move the lens relative to the sensor (or vice versa) in the opposite direction. This approach compensates for hand-shake and general user motion during the image capturing process, although there are limitations on the performance of optical stabilizers as well.
Another approach to addressing the hand-shake blur problem is to actually restore a blurred image digitally. For this approach, PSF must be known. The PSF can be measured using gyro type circuitry, such as that found in higher- or middle-end cameras. However use of a gyro results in a commensurate increase in the cost of the camera, and is therefore not ideal.
Therefore, it is desirable to have a method for estimating the PSF from the blurred image itself that does not rely on the use of an accelerometer or gyro-type circuitry.
To that end, there are known methods for estimating the PSF from a single motion blurred image. Some methods assume that the motion is constant and that the PSF can be characterized by only two parameters—the length and the angle of inclination. However, this assumption is unrealistic and does not accurately represent the PSF for most real-life conditions. Other PSF estimation techniques require a global analysis of the image and rely on the statistical properties of natural images, such as the distribution of gradients or behavior of the Fourier transformation.
However, there remains a need in the art for a PSF estimation technique that does not rely on a gyro, but is still robust, accurate, and simple enough for implementation in a consumer-level digital camera.